Large scale weather and wind forecasts, employed, for example, in applications such as aircraft flight planning, nautical navigation and wildfire fighting, are becoming more and more exact and refined. However, there are still airborne wind forecasting issues which must be addressed. Conventional forecasting systems take an ensemble approach to wind and weather forecasting. Ensemble approaches take multiple weather and wind forecasts generated by multiple entities or sources such as the National Weather Service, Met Office (UK weather forecasting service) or other public or private meteorological forecasting services. These conventional forecasting systems result in many different forecasts, some of which can deviate wildly from the others. When used, for example, in the field of aircraft flight plan generation, conventional forecasting systems using ensemble forecasts can result in the prediction of multiple different flights, each corresponding to each ensemble forecast. Each flight plan generated by ensemble forecasts also has contingencies calculated (e.g., extra fuel necessary in case the weather or wind deviates from the forecast). With all of the different contingency fuel calculated, a distribution of the different contingency fuel calculations is used to determine the spread of possible contingency fuel. If the spread is small, conclusions can be drawn that the weather conditions forecasted by the ensemble forecast are not volatile enough to justify an excess amount of contingency fuel to be carried.
Conventional forecasting systems using ensemble forecast techniques are typically computationally expensive to calculate (for example, ensemble forecast systems have to calculate multiple flight plans within the flight planning field). Conventional forecasting systems also cannot gauge explicit uncertainty in the most likely forecast, and are only designed to gauge the spread of potential contingencies. Further, conventional forecasting systems cannot operate with a single forecast, and instead must rely on multiple forecasts. Each ensemble forecast used in conventional forecasting systems is equally probable, but this does not increase the accuracy of any single forecast in particular. Thus, the most likely forecast is not improved by conventional systems using conventional forecasting approaches.